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Gene coexpression network analysis for family studies based on a meta-analytic approach

BMC proceedings 2016年第SUPPL 7期10卷 119-123页

作者： Renaud Tissier Hae-Won Uh Erik van den Akker Brunilda Balliu Spyridoula Tsonaka Jeanine Houwing-Duistermaat Department of Medical Statistics and Bioinformatics Leiden University Medical Center PO Box 9600 2300 RC Leiden The Netherlands. Molecular epidemiology Leiden University Medical Centre Leiden The Netherlands Pattern Recognition & Bioinformatics Delft University of Technology Leiden The Netherlands. Department of Medical Statistics and Bioinformatics Leiden University Medical Center PO Box 9600 2300 RC Leiden The Netherlands Department of Statistics University of Leeds Leeds UK.

For a better understanding of the biological mechanisms involved in complex traits or diseases, networks are often useful tools in genetic studies: coexpression networks based on pairwise correlations between genes are commonly used. In case of a family-based design, it can be problematic when there is a large between-family variation in expression levels. We propose here a gene coexpression network analysis for family studies. We build a coexpression network for each family and then combine the results. We applied our approach to data provided for analysis in the Genetic Analysis Workshop 19 and compared it to 2 naïve approaches-ignoring correlations among the expressions and decorrelating the gene expression by using the residuals of a mixed model-and a single-probe analysis. Our approach seemed to better deal with heterogeneity with regard to the naïve approaches. The naïve approaches did not provide any significant results, while our approach detected genes via indirect effects. It also detected more genes than the single-probe analysis.

关键词： Systolic Blood Pressure Network Analysis Coexpression Network Gene Coexpression Network Shared Environmental Effect

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Metric learning for label fusion in multi-atlas based image segmentation

Metric learning for label fusion in multi-atlas based image ...

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IEEE International Symposium on Biomedical Imaging

作者： Hancan Zhu Hewei Cheng Xuesong Yang Yong Fan College of Mathematics Physics and Information Shaoxing University Shaoxing China School of Bioinformatics Chongqing University of Posts and Telecommunications Chongqing China National Laboratory of Pattern Recognition Chinese Academy of Sciences Beijing China Department of Radiology University of Pennsylvania Philadelphia PA USA

ISBN: (纸本)9781479923519

A novel metric learning method is proposed to fuse segmentation labels in multi-atlas based image segmentation. Different from current label fusion methods that typically adopt a predefined distance metric model to compute the similarity between image patches of atlas images and the image to be segmented, we learn a distance metric model from the atlases to keep image patches of the same structure close to each other while those of different structures are separated. The learned distance metric model is then used to compute the similarity measure between image patches in the label fusion. The proposed method has been validated for segmenting hippocampus based on the EADC-ADNI dataset and the experimental results have demonstrated that our method can achieve better segmentation performance than the existing weighted voting label fusion methods with predefined metric models.

关键词： Measurement Image segmentation Hippocampus Training Indexes Computational modeling Image registration

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OUGENE： a disease associated over-expressed and under-expressed gene database

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Science Bulletin 2016年第10期61卷 752-754页

作者： Xiaoyong Pan Hong-Bin Shen Section for Animal Genetics Bioinformatics and Breeding Center for Non-Coding RNA in Technology and Health University of Copenhagen 1870 Frederiksberg Denmark Department of Disease Systems Biology Novo NordiskFoundation Center for Protein Research University ofCopenhagen 1870 Frederiksberg Denmark Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

Gene over-expression or under-expression is closely associated with human diseases, which contributes to phenotypic variations and diversity. To our best knowledge, there is no single open specific resource available to provide the association information between gene over- or under-expression and various diseases. In this study, we presented a comprehensive disease-associated over- and under-expressed gene database （OUGene） based on our proposed text mining pipeline and several open curated databases. It contains total 41,269 unique associa- tions between 7,238 over- or under-expressed genes and 1,480 diseases, which are supported by 81,974 evidence sentences from 56,442 articles. The OUGene is compre- hensive and covers most important therapeutic areas. Meanwhile a new scoring system is designed to rank the associations based on benchmarking against hand-curated data. OUGene provides an easy-of-use web interface for researchers to analyze these data and visualize the associ- ated networks, which can give insights to the complex relationships between over- and under-expressed genes and diseases at a system level. It is available at ***. ***/bioinf/OUGene/.

关键词： Text mining Gene over- and under-expression Disease Database

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Correction to: A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer's disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity

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Acta neuropathologica 2020年第5期139卷 959-962页

作者： Sven J van der Lee Olivia J Conway Iris Jansen Minerva M Carrasquillo Luca Kleineidam Erik van den Akker Isabel Hernández Kristel R van Eijk Najada Stringa Jason A Chen Anna Zettergren Till F M Andlauer Monica Diez-Fairen Javier Simon-Sanchez Alberto Lleó Henrik Zetterberg Marianne Nygaard Cornelis Blauwendraat Jeanne E Savage Jonas Mengel-From Sonia Moreno-Grau Michael Wagner Juan Fortea Michael J Keogh Kaj Blennow Ingmar Skoog Manuel A Friese Olga Pletnikova Miren Zulaica Carmen Lage Itziar de Rojas Steffi Riedel-Heller Ignacio Illán-Gala Wei Wei Bernard Jeune Adelina Orellana Florian Then Bergh Xue Wang Marc Hulsman Nina Beker Niccolo Tesi Christopher M Morris Begoña Indakoetxea Lyduine E Collij Martin Scherer Estrella Morenas-Rodríguez James W Ironside Bart N M van Berckel Daniel Alcolea Heinz Wiendl Samantha L Strickland Pau Pastor Eloy Rodríguez Rodríguez Bradley F Boeve Ronald C Petersen Tanis J Ferman Jay A van Gerpen Marcel J T Reinders Ryan J Uitti Lluís Tárraga Wolfgang Maier Oriol Dols-Icardo Amit Kawalia Maria Carolina Dalmasso Mercè Boada Uwe K Zettl Natasja M van Schoor Marian Beekman Mariet Allen Eliezer Masliah Adolfo López de Munain Alexander Pantelyat Zbigniew K Wszolek Owen A Ross Dennis W Dickson Neill R Graff-Radford David Knopman Rosa Rademakers Afina W Lemstra Yolande A L Pijnenburg Philip Scheltens Thomas Gasser Patrick F Chinnery Bernhard Hemmer Martijn A Huisman Juan Troncoso Fermin Moreno Ellen A Nohr Thorkild I A Sørensen Peter Heutink Pascual Sánchez-Juan Danielle Posthuma Jordi Clarimón Kaare Christensen Nilüfer Ertekin-Taner Sonja W Scholz Alfredo Ramirez Agustín Ruiz Eline Slagboom Wiesje M van der Flier Henne Holstege Alzheimer Center Amsterdam Department of Neurology Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands. s.j.vanderlee@amsterdamumc.nl. Department of Clinical Genetics Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands. s.j.vanderlee@amsterdamumc.nl. Department of Neuroscience Mayo Clinic Florida Jacksonville FL 32224 USA. Alzheimer Center Amsterdam Department of Neurology Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands. Department of Complex Trait Genetics Center for Neurogenomics and Cognitive Research Amsterdam Neuroscience Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam The Netherlands. Department for Neurodegenerative Diseases and Geriatric Psychiatry University of Bonn Bonn Germany. DZNE German Center for Neurodegenerative Diseases Bonn Germany. Division of Neurogenetics and Molecular Psychiatry Department of Psychiatry and Psychotherapy Faculty of Medicine University Hospital Cologne Cologne Germany. Molecular Epidemiology Leiden University Medical Center Leiden The Netherlands. Pattern Recognition and Bioinformatics Delft University of Technology Delft The Netherlands. Research Center and Memory Clinic Fundació ACE Institut Català de Neurociències Aplicades Universitat Internacional de Catalunya Barcelona Spain. Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED) Madrid Spain. Department of Neurology Brain Center Rudolf Magnus University Medical Center Utrecht Utrecht The Netherlands. Amsterdam UMC-Vrije Universiteit Amsterdam Department of Epidemiology and Biostatistics Amsterdam Public Health Research Institute Amsterdam The Netherlands. Interdepartmental Program in Bioinformatics University of California Los Angeles USA. Neuropsychiatric Epidemiology Unit Department of Psychiatry and Neurochemistry Institute of Neuroscience and Physiology Sahlgrenska Academy Centre for Ageing and Health (AgeCap) at

The IPDGC (The International Parkinson Disease Genomics Consortium) and EADB (Alzheimer Disease European DNA biobank) are listed correctly as an author to the article, however, they were incorrectly listed more than o...

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Deep convolutional neural networks as generic feature extractors

Deep convolutional neural networks as generic feature extrac...

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International Joint Conference on Neural Networks (IJCNN)

作者： Lars Hertel Erhardt Barth Thomas Käster Thomas Martinetz Institute for Neuro-and Bioinformatics University of Luebeck Germany Pattern Recognition Company GmbH Luebeck Germany

Recognizing objects in natural images is an intricate problem involving multiple conflicting objectives. Deep convolutional neural networks, trained on large datasets, achieve convincing results and are currently the state-of-the-art approach for this task. However, the long time needed to train such deep networks is a major drawback. We tackled this problem by reusing a previously trained network. For this purpose, we first trained a deep convolutional network on the ILSVRC-12 dataset. We then maintained the learned convolution kernels and only retrained the classification part on different datasets. Using this approach, we achieved an accuracy of 67.68% on CIFAR-100, compared to the previous state-of-the-art result of 65.43%. Furthermore, our findings indicate that convolutional networks are able to learn generic feature extractors that can be used for different tasks.

关键词： Convolution Kernel Handwriting recognition Art

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Speeding Up Tracking by Ignoring Features

Speeding Up Tracking by Ignoring Features

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IEEE Conference on Computer Vision and pattern recognition

作者： Lu Zhang Hamdi Dibeklioglu Laurens van der Maaten Pattern Recognition & Bioinformatics Group Delft University of Technology

ISBN: (纸本)9781479951192

Most modern object trackers combine a motion prior with sliding-window detection, using binary classifiers that predict the presence of the target object based on histogram features. Although the accuracy of such trackers is generally very good, they are often impractical because of their high computational requirements. To resolve this problem, the paper presents a new approach that limits the computational costs of trackers by ignoring features in image regions that - after inspecting a few features - are unlikely to contain the target object. To this end, we derive an upper bound on the probability that a location is most likely to contain the target object, and we ignore (features in) locations for which this upper bound is small. We demonstrate the effectiveness of our new approach in experiments with model-free and model-based trackers that use linear models in combination with HOG features. The results of our experiments demonstrate that our approach allows us to reduce the average number of inspected features by up to 90% without affecting the accuracy of the tracker.

关键词： Target tracking Feature extraction Computational modeling Videos Upper bound Accuracy

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Graph-Based Kinship recognition

Graph-Based Kinship Recognition

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International Conference on pattern recognition

作者： Yuanhao Guo Hamdi Dibeklioglu Laurens Van Der Maaten Section Imaging & Bioinformatics Leiden University The Netherlands Pattern Recognition & Bioinformatics Group Delft University of Technology The Netherlands Pattern Recognition & Bioinf. Group Delft Univ. of Technol. Delft Zuid-Holland NL

ISBN: (纸本)9781479952106

Image-based kinship recognition is an important problem in the reconstruction and analysis of social networks. Prior studies on image-based kinship recognition have focused solely on pair wise kinship verification, i.e. on the question of whether or not two people are kin. Such approaches fail to exploit the fact that many real-world photographs contain several family members, for instance, the probability of two people being brothers increases when both people are recognized to have the same father. In this work, we propose a graph-based approach that incorporates facial similarities between all family members in a photograph in order to improve the performance of kinship recognition. In addition, we introduce a database of group photographs with kinship annotations.

关键词： Databases Feature extraction Face recognition Image recognition Accuracy Estimation Prediction algorithms

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The role of color and contrast in facial age estimation

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2014年 8749卷 52-61页

作者： Dibeklioğlu, Hamdi Gevers, Theo Lucassen, Marcel Salah, Albert Ali Intelligent Systems Lab Amsterdam University of Amsterdam Netherlands Pattern Recognition and Bioinformatics Group Delft University of Technology Netherlands Department of Computer Engineering Boğaziçi University Istanbul Turkey

Computer based methods for facial age estimation can be improved by incorporating experimental findings from human psychophysics. Moreover, the latter can be used in creating systems that are not necessarily more accurate in age estimation, but strongly resemble human age estimations. In this paper we investigate the perceptual hypothesis that contrast is a useful cue for estimating age from facial appearance. Using an extensive evaluation paradigm, we establish that using a perceptual color space improves computer’s age estimation, and more importantly, using contrast-enabled features results in estimations that are more correlated to human estimations. © Springer International Publishing Switzerland 2014.

关键词： Estimation

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Key-point detection with multi-layer center-surround inhibition

Key-point detection with multi-layer center-surround inhibit...

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International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Foti Coleca Sabrina Zîrnovean Thomas Käster Thomas Martinetz Erhardt Barth Institute for Neuro- and Bioinformatics University of Lübeck Lübeck Germany University “POLITEHNICA” of Bucureşti Bucureşti Romania Pattern Recognition Company GmbH Innovations Campus Lübeck Lübeck Germany

ISBN: (纸本)9781479976867

We present a biologically inspired algorithm for key-point detection based on multi-layer and nonlinear center-surround inhibition. A Bag-of-Visual-Words framework is used to evaluate the performance of the detector on the Oxford III-T Pet Dataset for pet recognition. The results demonstrate an increased performance of our algorithm compared to the SIFT key-point detector. We further improve the recognition rate by separately training codebooks for the ON- and OFF-type key points. The results show that our key-point detection algorithms outperform the SIFT detector by having a lower recognition-error rate over a whole range of different key-point densities. Randomly selected key-points are also outperformed.

关键词： Detectors Feature extraction Positron emission tomography Training Image edge detection Biological information theory Ions

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Barnes-Hut-SNE 1

Barnes-Hut-SNE

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1st International Conference on Learning Representations, ICLR 2013

作者： van der Maaten, Laurens Pattern Recognition and Bioinformatics Group Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

The paper presents an O(N log N)-implementation of t-SNE — an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots and that normally runs in O(N2). The new implementation uses vantage-point trees to compute sparse pairwise similarities between the input data objects, and it uses a variant of the Barnes-Hut algorithm to approximate the forces between the corresponding points in the embedding. Our experiments show that the new algorithm, called Barnes-Hut-SNE, leads to substantial computational advantages over standard t-SNE, and that it makes it possible to learn embeddings of data sets with millions of objects. © 2013 International Conference on Learning Representations, ICLR. All rights reserved.

关键词： Embeddings

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